One of Big Datas Giants Accused of Big Time Fraud

January 15, 2018

Palantir, one of the biggest names in big data has been praised for its innovative solutions since it began 2004. However, it has been getting attention for all the wrong reasons lately, as we saw in a recent Deal Street Asia story, “Palantir Holder Says Company Sabotaged Stock Sale to Chinese.”

One of Palantir Technologies Inc.’s early investors accused the data-mining startup of sabotaging his attempt to sell his $60 million stakes to a Chinese company so directors and executives could enrich themselves by selling their stock instead.

Marc Abramowitz, a 63-year-old lawyer and investor, contends that when Palantir executives got wind of his offer to sell his stock to Chinese private equity firm CDH Investments Fund Management Co., they sunk the deal by offering to sell their shares to CDH instead, according to a lawsuit filed Thursday in Delaware. Palantir’s campaign to spoil Abramowitz’s sale demonstrates the Silicon Valley company’s “willingness to intentionally interfere with shareholder transactions in an effort…’

It may be tough to prove this in court, however. Palantir is famous for its secrecy, though that may become a thing of the past when they go public. Either way, this is an interesting look at the cutthroat world of big data and the potential things people do to stay on top.

Patrick Roland, January 15, 2018

Alphabet Google Is Allegedly Inventing the Future

January 14, 2018

I read an interesting round up of Alphabet Google’s research initiatives. The article’s title is “The Future According to Alphabet Moonshots: From Calico to X.”

The write up begins by pointing out that Alphabet Google has trimmed some of its notable explorations. I learned:

Alphabet has already started to shed some of its less successful side projects, suggesting the holding company will only tolerate so much discomfort. In the past year, it sold satellite imaging firm Terra Bella and terrifying robotics division Boston Dynamics, while shuttering down solar-powered, internet-by-drone idea Titan and modular smartphone Project Ara.

Yep, cost control exists at Alphabet Google.

The list of “inventions” for the future mentioned in the article includes:

  • Calico which is part of the “solving death” thing
  • CapitalG, which is another of Alphabet Google’s “invest in others” activities
  • DeepMind, which is Alphabet Google’s smart software outfit
  • Jigsaw, the company’s “tech accelerator.”
  • Project Jacquard which is focused on weaving electronics into clothing
  • Project Soli which is a sensor “that uses radar to track miniscule motions”
  • Sidewalk Labs, which is Google’s smart city play
  • Verily, which seems to be another component of the “solving death”, immortality initiative
  • Waymo, which is the self driving auto initiative
  • X, which is the home of the Loon Balloon and “early stage trials”

Several thoughts crossed my mind as I worked through this list.

  1. Google is largely dependent on online advertising for its revenues. After 20 years of investing and inventing, the company still remains dependent on an idea inspired by GoTo.com. You know trend lines reveal quite a bit about the past and the future. This trend line suggests that Alphabet Google is an online advertising company with little success in diversification or leapfrogging.
  2. Alphabet Google in into a wide spectrum of technologies. The unifying theme of these inventions, bets, or moonshots is not evident to me. The analogy is a person who has money betting on many different long odds lottery games.
  3. The artificial intelligence plays like DeepMind do not allow Alphabet Google to deal with malware in the Android store, filter YouTube videos for certain proscribed content for children, and cope with Google Images penchant for returning oddball search results. (Try male bikini without parental filters enabled.)

Without doing any additional research, I think that Alphabet Google is demonstrating that some Internet start up ideas do not enable additional revenue streams by throwing money at many bets. The old Bell Labs pulled off this trick but so far Google has not been able to duplicate Bell Telephone’s success in innovations that stick, then diffuse, and ultimately create new businesses.

Alphabet Google’s principal mechanism for innovation is the thousands of former Google employees who have left the company and pursued their own ideas. A good example is the Xoogler magnet Facebook.

Also, will Alphabet Google be been able to match Amazon’s revenue diversification?

Is Alphabet Google inventing the future? Yes, as long as it hires smart people who leave the company. The internal track record is interesting, but it has done little to allow the company to shake its addiction to online ad revenue.

What happens if that ad revenue softens, faces regulation in Europe and elsewhere, and erodes the online search value statement?

Has Alphabet Google’s bets created a situation in which the company must dog paddle frantically to maintain the status quo?

Stephen E Arnold, January 14, 2018

Bye-Bye Silicon Valley Monopoly

December 14, 2017

Silicon Valley is a technology epicenter and used to be synonymous with modern innovation, but that is no longer the case.  CNBC reports that, “Billionaire Investor Peter Thiel: Silicon Valley’s Monopoly On Big Growth Tech Companies Is Over.”   Peter Thiel is a famous Silicon Valley investor.  He helped launch PayPal, was an early investor in Facebook and Airbnb, and he also launched Palantir Technologies.  As one of the top Silicon Valley insiders, he said that:

‘I have been investing in the technology space — entrepreneur and investor over the past 20 years in Silicon Valley — and within the area of IT, it has for the last 10, 15 years in the US and the world been extremely centered on Silicon Valley,’ Thiel says, speaking at the Future Investment Initiative in Riyadh, Saudi Arabia, Thursday.  ‘I think there are a lot of reasons for that, but the question is, ‘Where is the growth going to happen the next 10 years?’ And what I would tend to think is that it will be more diversified from just Silicon Valley.’

Thiel continued that technology startups can be built anywhere, you just need the right people, money, and the right governance structures.  He was surprised that so many technology businesses popped up in Silicon Valley, but that happened because of the number of mentors and entrepreneurship concentrated in one area.  Innovators went where the action was happening.  It is similar to how actors go to Hollywood and writers head to New York City.

Thanks to Silicon Valley, technology has changed the world, so the next venture company can be located anywhere.  Take a guess about where the next big technology might be or if it will be spread out along the grid.

Whitney Grace, December 14, 2017

Knowledge Supposedly the Best Investment

December 13, 2017

Read, read, read, read!  You are told it is good for you, but, much like eating vegetables, no one wants to do it.  School children loath their primers, adults say they do not have the time, and senior citizens explain it puts them to sleep.  Reading, however, is the single best investment an individual can make.  This is not new, but the Observer treats reading like some epiphany in the article, “If You’re Not Spending Five Hours Per Week Learning, You’re Being Irresponsible.”

The article opens with snippets about famous smart people and how they take the time to read at least an hour a day.  The stories are followed by these wise words:

The answer is simple: Learning is the single best investment of our time that we can make. Or as Benjamin Franklin said, ‘An investment in knowledge pays the best interest.’  This insight is fundamental to succeeding in our knowledge economy, yet few people realize it. Luckily, once you do understand the value of knowledge, it’s simple to get more of it. Just dedicate yourself to constant learning.

The standard excuse follows that in today’s modern world we are too busy making money in order to survive to learn new things, then we are slugged with the dire downer that demonetization is making previously expensive technology cheaper or even free.  Examples are provided such as video conferencing, video game consoles, cameras, encyclopedias, and anything digital.  All of these are found on a smartphone.

Technology that was once gold is now cheap, making knowledge more valuable.  Then we are told that technology will make certain jobs obsolete and the only way to survive in the future will be to gain more knowledge and apply, because this can never be taken from you. The bottom line is to read, learn, apply knowledge, and then make that a daily ritual.  The message is not anything new, but does learning via filtered and censored online search results count?

Whitney Grace, December 13, 2017

Free Language Learning Resources That Are Not Duolingo

October 25, 2017

For those who wish to learn a foreign language, the fun and engaging Duolingo has become a go-to free resource, offering courses in more than 20 languages. However, it is not the only game in town; MakeUseOf  gives us a rundown of “The Best (Completely Free) Language Learning Alternatives to Duolingo.” Writer Briallyn Smith tells us:

One of the reasons some people are looking to move away from Duolingo is the recent introduction of in-app purchases. While the core functions of Duolingo are still free, the purchase options can give learners a boost when playing games — much like the bonuses and extra lives you can purchase on Bejewelled or other addictive gaming apps. Learners may become frustrated when they are prevented from working on a specific language skill or accomplishment because they ran out of ‘hearts’ or need to purchase ‘gems’ to continue. Other in-app purchases allow users to remove ads from their learning experience and to download offline content.

While there’s nothing wrong with Duolingo charging fees for its services, it can be frustrating for those looking for a truly free resource. Other language learners simply do not enjoy learning through games. This is especially true for those who require industry-specific vocabulary or who already have a background in the language. Thankfully, there are many other online resources available for language learners. While you won’t get the same kind of program as Duolingo for free, you can easily use these resources to put together a language learning strategy that works well for you.

Before getting to her list, Smith takes a moment to advocate for paid language-learning services, like Babbel. Basically, if you are serious about your language studies and can afford it, they are worth the investment.

The resource list begins with a compound entry, Online Communities; included here are Fluent in 3 Months/r/LanguageLearning, and The Polyglot Club. Then there are Rhino Spike, Mango Languages, the Yojik Website, and, of course, YouTube (with a list of 10 suggested channels). Furthermore,  Smith supplies a link to OpenCulture for more even options. See the article for more about each of these entries.

Cynthia Murrell, October 25, 2017

Big Data and Big Money Are on a Collision Course

October 16, 2017

A recent Forbes article has started us thinking about the similarities between long-haul truckers and Wall Street traders. Really! The editorial penned by JP Morgan, “Informing Investment Decisions Using Machine Learning and Artificial Intelligence,” showcases the many ways in which investing is about to be overrun with big data machines. Depending on your stance, it is either thrilling or frightening.

The story claims:

Big data and machine learning have the potential to profoundly change the investment landscape. As the quantity and the access to data available have grown, many investors continue to evaluate how they can leverage data analysis to make more informed investment decisions. Investment managers who are willing to learn and to adopt new technologies will likely have an edge.

Sounds an awful lot like the news we have been reading recently about how almost two million truck drivers could be out of work in the next decade thanks to self-driving cars. If you have money in trucking, the amount saved is amazing, but if that’s how you make your living things have suddenly become chilly. Sounds like the future of Wall Street, according to this story.

It continues:

Big data and machine learning strategies are already eroding some of the advantage of fundamental analysts, equity long-short managers and macro investors, and systematic strategies will increasingly adopt machine learning tools and methods.

If you ask us, it’s not a matter of if but when. Nobody wants to lose their job due to efficiency, but it’s pretty much impossible to stop. Money talks and saving money talks loudest to companies and business owners, like investment firms.

Patrick Roland, October 16, 2017

Tech Industry Toxicity Goes Beyond Uber

September 13, 2017

Shiny new things have distracted people from certain behaviors, and Fast Company is calling out the entire technology startup culture in, “Why Silicon Valley Can’t Call Uber an Anomaly.” Writer Austin Carr takes us briefly through Uber’s tribulations, which culminated in the departure of infamous CEO Travis Kalanick. See the article for that useful summary, but Carr’s question was whether Uber’s noxious culture is unusual. He writes:

Silicon Valley, though, is insular and guarded. In my reporting, I encountered few people willing to speak openly, let alone critically, about Uber’s troubles. Those who did (most of them, notably, women) argue that there’s an opportunity for course correction right now. It starts by acknowledging that the Valley isn’t yet the utopian meritocracy it strives to be—and that Uber’s errant system exposed some fundamental bugs in the startup economy.

Carr identifies and discusses three of these bugs. First, that which makes a startup succeed often does not scale up well. For example, a confrontational culture that pits workers against each other might fuel a startup’s launch, but becomes unsustainable in a large, global corporation. The second problem is the myth of the “omniscient founder.” Though most of us realize that generating a brilliant idea does not necessarily go hand-in-hand with the capacity to run a large organization, much of the tech industry still seems taken by the foolish notion of one man at the top skillfully managing each and every aspect of the business. Carr points out that even Steve Jobs and Larry Page saw the wisdom in stepping back, and each tapped someone with more corporate experience to run their companies for a while. Not only is this hero-at-the-top attitude inefficient, it also risks the devaluation of every other employee. Talent does not stay where it is not respected.

Finally, Carr observes, the system of accountability needs an overhaul. It takes a lot of scandals to push investors to hold tech companies accountable for bad behavior, and even then board members hesitate to act. The article concludes:

If there really were healthy checks and balances, boards wouldn’t wait for public outrage to act. But to acknowledge that Uber’s system of accountability failed is to acknowledge that fundamental change—something Silicon Valley normally embraces—is necessary. If the Valley truly prides itself on moving fast and breaking things, it ought to start here.

We are curious to see how the industry will respond to such escalating criticisms.

Cynthia Murrell, September 13, 2017

Support for Open Source AI from Financial Firms

August 31, 2017

Financial tech reporter Ian Allison at the International Business Times finds it interesting that financial services firms are joining tech companies like Google and Microsoft in supporting open source AI solutions. In his piece, “Finance and Artificial Intelligence Are Going ‘Fintech’ and Open Source,” Allison points to one corporate software engineer as instrumental to the trend:

QR Capital Management was probably patient zero when it came to opening up their code around data storage – and this move, shepherded by software engineer Wes McKinney, kickstarted the popular Pandas libraries project. Now he has returned to open source work at Two Sigma. We have also seen open source data storage offerings coming out of Man AHL in the form of Arctic. Taking part in a panel on open source infrastructure, McKinney said investment in an open source project yields dividends later: data storage underlies other verticals, and when other people use the software and build libraries on top of it, that makes in-house systems more compatible.

See this link for more about the panda’s library. In the same panel Allison cites above, participants were asked how best to sustain the open source community. McKinney gave this advice:

I feel a compulsion not to let open source projects die. But without sponsorship it can become hard to sustain. So when commercials ask me how they can help, I say sponsor an individual – to triage issues, do patches; that goes a long way.

So, what industry will be next to throw its weight behind open source projects?

Cynthia Murrell, August 31, 2017

 

The Tech Unicorn Ploy

August 28, 2017

This should not come as much of a surprise— Business Insider reports, “Nearly Half of Tech ‘Unicorns’ Rely on Tricky Math to Land Imaginary Valuations.” So dubbed because they were once rare, “unicorn” startups are ones that have achieved valuations of at least a billion dollars. That is “billion” with a “b.” According to a pair of business professors (from the UBC Sauder School of Business and the Stanford  Graduate School of Business), there are now more than 200 such “rare” prospects globally. Why the apparent boom in unicorn birth rates? Citing a recent study put out by the above-mentioned professors, reporter Alex Morrell writes:

Many of [these startups] are using creative financing maneuvers to conjure imaginary valuation figures that don’t hold up to scrutiny, according to the UBC/GSB study, which examined 116 unicorns. It turns out, when you adjust the valuations to account for guarantees provided to preferred shareholders that dilute the value of common shares, nearly half of unicorns lose their coveted $1 billion status.

The article links to an interview with Will Gornall, the professor from UBC Sauder, that explains how he and co-researcher Ilya Strebulaev re-evaluated purported unicorns to discount the influence of such preferred-shareholder guarantees. They found nearly half sported fake horns, with 11% having been valued at more than twice their fair values. The article continues:

Here’s how it works: In later funding rounds, startups will negotiate a higher share price, but as part of the bargain they guarantee their investors certain protections — such as earning a minimum return on their money or guaranteeing they’ll be paid out in full before all other shareholders. ‘Specifically, we found that 53 per cent of unicorns gave their most recent investors either a return guarantee in IPO (14%), the ability to block IPOs that did not return most of their investment (20%), seniority over all other investors (31%), or other important terms,’ Gornall said. Even though this sort of thing has become normal, valuations haven’t caught up to the fact that providing additional protections to senior shareholders lessens the value of common shareholders. Treating the shares equally can significantly inflate the overall value of the company.

Overvaluation can, of course, help a startup attract funding, talent, and customers. For employees, however, such tactics can end up devaluing their compensation packages. Both workers and investors should be wary of over-valuation trickery.

Cynthia Murrell, August 28, 2017

Banks Learn Sentiment Analysis Equals Money

July 26, 2017

The International Business Times reported on the Unicorn conference “AI, Machine Learning and Sentiment Analysis Applied To Finance” that discussed how sentiment analysis and other data are changing the financing industry in the article: “AI And Machine Learning On Social Media Data Is Giving Hedge Funds A Competitive Edge.”  The article discusses the new approach to understanding social media and other Internet data.

The old and popular method of extracting data relies on a “bag of words” approach.  Basically, this means that an algorithm matches up a word with its intended meaning in a lexicon.  However, machine learning and artificial intelligence are adding more brains to the data extraction.  AI and machine learning algorithms are actually able to understand the context of the data.

An example of this in action could be the sentence: “IBM surpasses Microsoft”. A simple bag of words approach would give IBM and Microsoft the same sentiment score. DePalma’s news analytics engine recognises “IBM” is the subject, “Microsoft” is the object and “surpasses” as the verb and the positive/negative relationships between subject and the object, which the sentiment scores reflect: IBM positive, Microsoft, negative.

This technology is used for sentiment analytics to understand how consumers feel about brands.  In turn, that data can determine a brand’s worth and even volatility of stocks.  This translates to that sentiment analytics will shape financial leanings in the future and it is an industry to invest in

Whitney Grace, July 26, 2017

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